Apparatus for automated identification of thick cell groupings on a biological specimen

ABSTRACT

A thick group of cells classifier. Image data acquired from an automated microscope from a cytological specimen is processed by a computer system. The computer applies filters at different stages. Obvious artifacts are eliminated from analysis early in the processing. The first stage of processing is image segmentation where objects of interest are identified. The next stage of processing is feature calculation where properties of each segmented thick group object are calculated. The final step is object classification where every segmented thick group object is classified as being abnormal or as belonging to a cellular or non-cellular artifact.

This application is a continuation of application Ser. No. 08/309,116,filed on Sep. 20, 1994, now abandoned.

The invention relates to an automated cytology system and, moreparticularly, to the automated identification of thick cell groups on abiological specimen such as a Papanicolaou prepared gynecological smear.

BACKGROUND OF THE INVENTION

One objective for Papanicolaou smear analysis is to emulate the wellestablished human review process which follows standards recommended byThe Bethesda System. A cytologist first views a slide at lowmagnification to identify areas of interest, then views those areas ofinterest at high magnification where it is possible to distinguish cellsaccording to their structure and context.

The prior art has found it difficult to extract information from thickgroups of cells, and, until the present invention, has not usedinformation from thick groups of cells as a diagnostic tool.

It is therefore a motive of the invention to provide a method andapparatus to identify thick groups of cells on a biological specimen.

SUMMARY OF THE INVENTION

The invention provides a thick group analysis apparatus that performsmultiple layers of processing. As image data is processed by theapparatus of the invention, the image data passes through variousstages. Each stage applies filters that provide finer and finer detail.Each stage of processing incorporates more information into the decisionprocess. The invention eliminates obviously undesirable artifacts earlyin the stages of processing. As specific objects progress through thefilters, the invention makes more detailed evaluations of the object.The invention rapidly reduces the amount of data to be reviewed so thatmore time consuming techniques are used on a relatively small portion ofthe original data. Each filter is designed to retain a large percentageof the data of interest while rejecting as much as possible of theunavailing data. As more filters are passed, the likelihood increasesthat an area of interest contains potentially abnormal cells.

The invention, comprises a set of image processing functions andstatistical decisions that are used to identify groups of cells that mayindicate a cancerous or pre-cancerous condition.

The invention detects the presence of certain normal and abnormal cellsthat tend to cluster in thick syncitium of cells in Papanicolaou-stainedcervical smears. The invention examines a slide that containspotentially abnormal thick groups of cells to help determine whether aslide needs to be reviewed by a cytopathologist or cytotechnologist.Information about potentially abnormal cell groups is used during slideclassification to provide supporting evidence for an anomaly score orquality control score determination.

Thick groups of cells are identified by decision rules that are computedfrom thousands of objects that were gathered from numerous training Papsmears. The construction of statistical decision rules is calledtraining. The data used in the construction of the rules is calledtraining data. Decision rules used include simple feature threshold andfeature threshold binary decision trees, linear, or Fisher's linearbinary decision trees.

To identify different classes of thick groups of cells, each decisionrule used numerical representations of group properties such as size,shape, texture, optical density and context as data. These groupproperties are called features.

Each decision rule uses the features of unknown thick groups of cellsand classifies the thick groups as a normal artifact or potentiallyabnormal group.

Slides are first processed at low magnification to detect possible areasof interest. These areas of interest are then examined at highmagnification. Images are processed to identify abnormalities and otherimportant cell types occurring in single isolated form, lightly ornon-overlapping clusters, or thick heavily overlapped syncitium. Theseanalysis are handled by the single cell, group and thick groupalgorithms, respectively, of which the later is disclosed in thispatent. As a cytologist compares size, shape, context, texture anddensity of cells against established criteria, so do the analyses ofcells according to criteria established during their training.

Other objects, features and advantages of the present invention willbecome apparent to those skilled in the art through the description ofthe preferred embodiment, claims and drawings herein wherein likenumerals refer to like elements.

BRIEF DESCRIPTION OF THE DRAWINGS

To illustrate this invention, a preferred embodiment will be describedherein with reference to the accompanying drawings.

FIGS. 1A, 1B and 1C show the automated cytology screening apparatus ofthe invention.

FIG. 2 shows a process flow diagram to process thick groups of cells.

FIG. 3 shows a process flow diagram to process single cell, group, andthick group analysis.

FIG. 4 shows a process flow diagram of the method of the invention toperform image segmentation.

FIG. 5 shows a process flow diagram of the training method of theinvention.

FIG. 6 shows a process flow diagram of the object classification methodof the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In a presently preferred embodiment of the invention, the systemdisclosed herein is used in a system for analyzing cervical pap smears,such as that shown and disclosed in U.S. Pat. No. 5,787,188, issued Jul.28, 1998 to Nelson et al., entitled "Method for Identifying NormalBiomedical Specimens", which is a file wrapper continuation of abandonedU.S. patent application Ser. No. 07/838,064, filed Feb. 18, 1992; U.S.Pat. No. 5,528,703, issued Jun. 18, 1996 to Lee, entitled "Method ForIdentifying Objects Using Data Processing Techniques", which is a filewrapper continuation of abandoned U.S. patent application Ser. No.07/838,395, filed Feb. 18, 1992; U.S. Pat. No. 5,315,700, issued May 24,1994 to Johnston et al., entitled "Method And Apparatus For RapidlyProcessing Data Sequences"; U.S. Pat. No. 5,361,140, issued Nov. 1, 1994to Hayenga et al., entitled "Method and Apparatus for Dynamic Correctionof Microscopic Image Signals"; and allowed U.S. patent application Ser.No. 08/302,355 for which the issue fee has been paid, filed September 7,1994 entitled "Method and Apparatus for Rapid Capture of FocusedMicroscopic Images" to Hayenga et al., which is a continuation-in-partof abandoned U.S. patent application Ser. No. 07/838,063 filed on Feb.18, 1992 the disclosures of which are incorporated herein, in theirentirety, by the foregoing references thereto.

The present invention is also related to biological and cytologicalsystems as described in the following patent applications which areassigned to the same assignee as the present invention, and which areall hereby incorporated by reference including pending U.S. Pat. No.5,757,954, issued May 26, 1998, to Kuan et al. entitled, "FieldPrioritization Apparatus and Method"; pending U.S. patent applicationSer. No. 08/927,379, filed Sep. 12, 1997 which is a file wrappercontinuation of abandoned U.S. patent application Ser. No. 08/309,061,to Wilhelm et al., entitled "Apparatus for Automated Identification ofCell Groupings on a Biological Specimen"; U.S. Pat. No. 5,787,189,issued Jul. 28, 1998 to Lee et al. entitled "Biological Analysis SystemSelf Calibration Apparatus, which is a file wrapper continuation ofabandoned U.S. patent application Ser. No. 08/309,115; U.S. Pat. No.5,828,776, issued Oct. 27, 1998, which is a file wrapper continuation ofabandoned U.S. patent application Ser. No. 08/308,992, to Lee et al.entitled "Apparatus for Identification and Integration of Multiple CellPatterns"; U.S. Pat. No. 5,627,908, issued May 6, 1997 to Lee et al.entitled "Method for Cytological System Dynamic Normalization"; U.S.Pat. No. 5,638,459, issued Jun. 10, 1997 to Rosenlof et al. entitled"Method and Apparatus for Detecting a Microscope Slide Coverslip"; U.S.Pat. No. 5,566,249, issued Oct. 15, 1996 to Rosenlof et al. entitled"Apparatus for Detecting Bubbles in Coverslip Adhesive," pending U.S.patent application Ser. No. 08/867,017, filed Jun. 3, 1997, which is afile wrapper continuation of abandoned U.S. patent application Ser. No.08/309,931, to Lee et al. entitled "Cytological Slide ScoringApparatus"; U.S. Pat. No. 5,692,066, issued Nov. 25, 1997, to Lee et al.entitled "Method and Apparatus for Image Plane Modulation PatternRecognition"; allowed U.S. patent application Ser. No. 08/309,250, toLee et al., for which the issue fee has been paid, entitled "Apparatusfor the Identification of Free-Lying Cells"; U.S. Pat. No. 5,740,269,issued Apr. 14, 1998 to Oh et al. entitled "A Method and Apparatus forRobust Biological Specimen Classification"; U.S. Pat. No. 5,715,327,issued Feb. 3, 1998, to Wilhelm et al. entitled "Method and Apparatusfor Detection of Unsuitable Conditions for Automated Cytology Scoring."

It is to be understood that the various processes described herein maybe implemented in software suitable for running on a digital processor.The software may be embedded, for example, in the central processor 540.

Now refer to FIGS. 1A, 1B and 1C which show a schematic diagram of oneembodiment of the apparatus of the invention for field of viewprioritization. The apparatus of the invention comprises an imagingsystem 502, a motion control system 504, an image processing system 536,a central processing system 540, and a workstation 542. The imagingsystem 502 is comprised of an illuminator 508, imaging optics 510, a CCDcamera 512, an illumination sensor 514 and an image capture and focussystem 516. The image capture and focus system 516 provides video timingdata to the CCD cameras 512, the CCD cameras 512 provide imagescomprising scan lines to the image capture and focus system 516. Anillumination sensor intensity is provided to the image capture and focussystem 516 where an illumination sensor 514 receives the sample of theimage from the optics 510. In one embodiment of the invention, theoptics may further comprise an automated microscope 511. The illuminator508 provides illumination of a slide. The image capture and focus system516 provides data to a VME bus 538. The VME bus distributes the data toan image processing system 536. The image processing system 536 iscomprised of field-of-view processors 568. The images are sent along theimage bus 564 from the image capture and focus system 516. A centralprocessor 540 controls the operation of the invention through the VMEbus 538. In one embodiment the central processor 562 comprises aMOTOROLA 68030 CPU. The motion controller 504 is comprised of a trayhandle 518, a microscope stage controller 520, a microscope turretcontroller 522, and a calibration slide 524. The motor drivers 526position the slide under the optics. A bar code reader 528 reads abarcode located on the slide. A touch sensor 530 determines whether aslide is under the microscope objectives, and a door interlock 532prevents operation in case the doors are open. Motion controller 534controls the motor drivers 526 in response to the central processor 540.An Ethernet communication system 560 communicates to a workstation 542to provide control of the system. A hard disk 544 is controlled byworkstation processor 550. In one embodiment, workstation processor 550may comprise a SUN SPARC CLASSIC™ workstation. A tape drive 546 isconnected to the workstation processor 550 as well as a modem 548, amonitor 552, a keyboard 554, and a mouse pointing device 556. A printer558 is connected to the ethernet 560.

During field of view prioritization, the central computer 540, running areal time operating system, controls the microscope 511 and theprocessor to acquire and digitize images from the microscope 511. Theflatness of the slide may be checked, for example, by contacting thefour corners of the slide using a computer controlled touch sensor. Thecomputer 540 also controls the microscope 511 stage to position thespecimen under the microscope objective, and from one to fifteen fieldof view (FOV) processors 568 which receive images under control of thecomputer 540.

During thick group analysis the computer system 540 receives input fromthree sources, as illustrated in FIG. 3.

20× FOV input images 18,

Results 21 from a single cell analysis 20,

Results 23 from a group algorithm 22, and

Results 25 from the group analysis 24.

Images at 20× magnification from the image acquisition module areprocessed by the computer 540. These are images the computer hasanalyzed at 4× magnification and have been determined to have alikelihood of containing clusters of cells. These cells may be normal orpotentially abnormal cells that tend to occur in thick groups. Theinvention is trained to recognize grouped cells that are so denselyclustered that their nuclei are difficult to differentiate. Cells thattend to occur in such thick groups include normal and abnormal cells ofthe endocervix and endometrium. For a complete list of the object typesthat were used for training, see Table 1.

The method of the invention classifies each of its segmented objects aseither abnormal, which are potentially abnormal thick groups, or other,which are cellular artifacts, non-cellular artifacts or normal cellgroups. Classification results from multiple analysis at 20×magnification are accumulated and used for slide classification. Thethick group process also receives whole image features from both thesingle cell analysis and endocervical group analysis to assist thickgroup classification.

The following table shows objects used for training of the classifier ofthick groups. Objects identified with an (A) were trained to beclassified as abnormal whereas objects denoted with an (O) wereclassified as other objects and disregarded.

                  TABLE 1                                                         ______________________________________                                        cellular objects      artifact objects                                        ______________________________________                                        (O) normal endometrial                                                                              (O)    mucus                                            (A) atypical endometrial hyperplasia                                                                (O)    bacteria                                         (A) endometrial adenocarcinoma                                                                      (O)    fibrous material                                 (O) normal endocervical                                                                             (O)    bubble edge                                      (A) AGUS              (O)    slide edge                                       (A) atypical endocervical                                                                           (O)    ground glass                                     (A) adenocarcinoma endocervical                                                                     (O)    graphite                                         (A) repair/reactive endocervical                                                                    (O)    not under                                                                     coverslip                                        (O) squamous cell groups                                                                            (O)    out of focus                                     (A) herpes            (O)    other                                            (A) adenocarcinoma in situ, endocx                                                                  (O)    inside bubble                                    (A) carcinoma in situ, squamous                                               (O) parabasal/metaplastic                                                     (O) cytoplasm only   (A) = abnormal                                           (A) adenocarcinoma  (O) = Other                                               (A) high grade SIL                                                            (O) lymphocytes                                                               (O) polys                                                                     (O) red blood cells                                                           (O) histiocytes                                                               (O) corn flaking                                                              ______________________________________                                    

Now refer to FIG. 2 which shows the thick group processing of theinvention. An image of the biological specimen and whole image featuresare obtained 17. The 20× magnification images are received from theimage capture and focus system 516, which is controlled by computer 562;some whole image features are received from the single cell analysis andgroup analysis.

The invention utilizes the following features from group classification.The magnitude of 2×1 dark edge in an FOV (feature #98).

These features are derived from single cell classification:

High mean (thick group feature #93)

Low threshold (thick group feature #95)

The classification results of thick group analysis are:

number of objects segmented

number of objects eliminated by box filters

number of objects eliminated by classification stages 1, 2 and 3

number of potentially abnormal objects remaining after the stage 3classifier

5-bin confidence histogram of remaining, potentially abnormal objects

Additionally, the invention performs error checking that does thefollowing:

Checks for proper return code from the classifiers.

Performs a checksum on the number of objects classified.

If an error is detected, the code aborts with an error message.

Prior to the thick group analysis the computer system 540 does thefollowing:

Detects coverslip edges and excludes from image processing all areasthat are outside of the area bounded by coverslip edges.

Accumulates slide level results from all 20× FOVs processed for thickgroups for each slide.

Provides the scores to the user interface.

Controls image acquisition and assures that images passed for thickgroup analysis conform to image quality specifications. The inventionchecks that images are acquired based on predetermined rules.

Handles errors if they are identified during thick group processing.

The thick group processing of the invention identifies certain kinds ofpotentially abnormal cells that tend to occur in thick groups. Thesethick groups are collections of cells that are so densely clustered thatit is difficult to distinguish individual nuclei. There are three majorsteps in processing:

Image Segmentation 12

Feature Calculation 14

Object Classification 16

Image segmentation 12 is the process of identifying objects of interestwithin a gray scale image and creating an object mask. An object mask isa binary image that represents the objects. Each area of interest isrepresented as active pixels in the object mask. Image segmentation,which is illustrated in more detail in FIG. 4, is a five step process.

The pre-processing stage 30 receives images 28 and eliminates singlecells and detects nuclear grouping information. This information is usedto integrate groups of cells into clusters. The information integrationprocess uses the following image processing sequence:

    ______________________________________                                                  Input image                                                                             Output image Structure element                            Operation location  location     and size                                     ______________________________________                                        Dilate    1L        1H           rod 7 × 1                              Erode     1L        1H           rod 9 × 1                              Dilate    1H        1H           rod 11 × 1                             Erode     1H        1H           rod 13 × 1                             Dilate    1H        1H           rod 15 × 1                             Erode     1H        1H           rod 17 × 1                             ______________________________________                                    

Objects are subjected to a sequence of dilations and erosions. In eachdilation and erosion operation, the structuring element is increased insize. This removes local, within cell, variations and isolates andhighlights global, or inter-cell grouping information.

The nuclear area weakening stage 32 detects the nuclear area by takingthe image created during the pre-processing step then subtracting theoriginal image from it. The detected nuclear area is added to thepre-processing image to remove isolated nuclei. This operation tries tofind thick cell group areas in which individual nuclei cannot bedelineated; it then derives object masks from these areas.

Non-nuclear area enhancement 34 is designed to improve connectivity ofthe clusters. A sequence of morphological operations detects non-nuclearareas that are in close proximity to nuclei. Next, the non-nuclear areais subtracted from the weakened nuclear area image. The subtractionoperation enhances the possibility of inclusion of these non-nuclearareas.

During thick group detection 36, a two-state conditional thresholdingprocess detects areas containing potential thick groups of cells. Firsta "less than" threshold is applied to the enhanced image to detect seedregions. Seed regions roughly define the shape of the thick group mask.Seed regions are dilated by a disk with a radius of 13 pixels. As this"less than" threshold value is increased, more pixels are detected. Ahigher threshold is applied to detect all potential thick group regions.These images are combined by finding pixels that are common to both thehigher threshold and dilated images.

Opening, which is a simple binary morphological operation, reshapesobjects to smooth boundaries and remove small regions in step 38. A diskwith a radius of 13 pixels is used as the structuring element for theopening. The result of opening is the final result of imagesegmentation, which creates thick group object masks 39.

Refer again to FIG. 2, Features are calculated according to apre-established protocol in step 14. Features are computed based eitheron portions of an image as defined by an object mask, or based on thewhole image. Object based features are numerical values that correspondto some aspects of a thick group's presentation. For example, area is afeature that gives the size, in pixels, of a thick group of cells; fov₋₋brightness is a whole image feature that provides information about theaverage brightness of an entire 20× FOV.

Object Classification 16 is accomplished using sets of features in alinear combination, then thresholding the result. A series of thesecombinations is integrated in a tree structure that together form aFisher's linear binary decision tree classifier. Several classifiers areused in series to form the overall algorithm. The goal is for earlyclassifiers to eliminate the majority of objects that are thick groupsof cells of normal cells or artifacts that resemble thick groups ofcells. The invention classifies these groups of objects as "other" 15.Objects that remain are more likely to be classified as potentiallyabnormal 11.

The invention eliminates 99% of artifact or normal cellular thick groupsof cells, while retaining 33% of potentially abnormal thick groups ofcells.

Many types of abnormal cellular conditions tend to form in thick groupsof cells. During training, the invention is designed to identify thefollowing cell group types as potentially abnormal: adenocarcinoma andatypia of the endocervix and endometrium, general adenocarcinoma,adenocarcinoma in situ, atypical glandular cell of unidentifiedsignificance (AGUS), repair and reactive states of endocervical cells,herpes, and high-grade squamous intraepithelial lesions. All these celltypes tend to appear in thick groups.

There are five major steps in invention training, as shown in FIG. 5starting at step 40. Step 42 is to build a library of features that canbe used to separate potentially abnormal objects from objects that areartifacts or normal cells. Step 44 acquires objects used to train agiven stage of processing. Step 46 computes features identified in thefeature library on new objects. Step 48 selects those features in thefeature library that most effectively separate objects in the trainingset. Step 50 builds a given stage of classifier based on the selectedfeatures. Steps 44 through 50 may be repeated several times until anoverall CM gain is reached for the classifier in step 52. The processthen stops 54. Feature identification, object acquisition, featureselection, and object classification are described later in thissection.

Thick group processing classifies as potentially abnormal those abnormalconditions listed in Table 1. In general, the invention discards normalcellular groups.

The invention uses features that are compatible with endocervical groupanalysis. However, the invention uses only those features that do notinvolve nuclear segmentation.

A wide range of features is necessary to properly discriminate segmentedgroups. Features used can be categorized in two different ways:

They can be identified by the kind of information they measure--objectshape, size, and texture, and so forth.

They can be identified by what part of an image they measure--the objectof interest, a small area around the object, or the whole image.

For algorithm training, about equal numbers of normal or artifactgroups, "other", and abnormal groups were acquired. The abnormalclassification includes all objects that are groups of potentiallyabnormal cells configured in thick groups. The other classificationincludes artifacts and groups of normal cells. See Table 1 foridentification of the abnormalities and other conditions used fortraining.

To manually acquire objects, a cytotechnologist screens abnormal slidesand circles areas containing abnormal thick groups. These slides arethen placed in the apparatus of the invention, and the abnormal thickgroup is positioned underneath the objective lens. The video camerascapture an image at 20×. Later, a cytopathologist verifies the diagnosisof the group. Once verified, these images become a part of the manualcell library that contains a number of images of conditions.

Other normal cell thick groups and all artifact groups were obtained byimplementing the classifier as a prototype machine running with normalslides. That process yielded a set of objects that passed the classifierat a given stage in its development. At the beginning of machinedevelopment, only the segmenter was implemented in code. The slides wereprocessed and fields of view were saved in which at least one thickgroup was segmented. These field of views were reviewed by acytotechnologist and placed in one of the object categories listed inTable 1.

Based on this data, a given stage in the classification process wasbuilt and coded. As indicated by FIG. 5, the process is again repeated,except now only those objects that pass the most recent classifier wereused to train the next stage.

Once a training set has been constructed for a given stage, it isnecessary to select the features that are best able to discriminatebetween object classes. Feature sets were determined using SAS' stepwisediscriminant analysis. The measure used to select features was Wilkes'lambda. A definition for this measure and the underlying theorygoverning the discriminant process is given in the SAS/STAT User'sGuide, Volume 2, pp 1493-1509.

Step 44 of FIG. 5 describes the process of constructing a data set thatis used to train a classifier. Each object is given a label as shown inTable 1. The task of the classifier is to establish decision boundariesso that the assigned classification most often matches the abnormal orother label given in Table 1. Selected features are used in a linearcombination and thresholded. When several such combinations are combinedin a binary tree structure, they form a Fisher's linear binary decisiontree classifier. For a more detailed account of the Fisher's lineardecision tree and the process used to build them, refer to the paper "ABinary Decision Tree Classifier" by Joo and Haralick in Machine VisionInternational, Feb. 19, 1986.

In addition to Fisher's linear decision trees, the thick group method ofthe invention uses box filters. These filters are implemented in theform:

    0=a0*(feature2)+a1-feature1

where a0, a1=constants

feature 1, feature2=feature values

If the expression is true, the object is classed as an artifact andstops further classification.

Box filters are trained on abnormal cell populations and specificartifact types because a significant portion of the artifact featuredistribution does not overlap with the abnormal distribution, even intwo-dimensional space. Therefore, box filters may be used to eliminate asubstantial portion of artifacts at a small expense in both processingtime and loss of potentially abnormal objects.

FIG. 6 shows a flowchart of the thick group analysis method of theinvention for object or whole image features 56. The graphic shows thatthe first step 58 is a series of box filters intended to reject obviousartifact groups. Next, three Fisher's linear decision tree classifiers(stages 1-3) are used in series to refine the classification ofpotentially abnormal thick groups. Note that three box filters 62precede the stage 2 classifier 71. These were defined because, afterartifact collection that followed the design of the stage one classifier60, a review of feature pair values indicated that these filters couldeliminate a large percentage of normal/artifact objects. Following aremore detailed descriptions of the classifiers used and the number ofsamples used to train them. Box Filters 58 are the first step in objectclassification. A series of box filters are designed to remove obviousartifacts. Features are used in pairs by the box filters, which arestructured to eliminate commonly occurring artifacts that may resemblesegmented groups of abnormal cells 74.

There are 10 box filters that are applied in six areas the number of aparticular type of filter appears in parenthesis. The features mentionedin the text below will be described under "Thick Group FeatureDescriptions" below.

Area box filter (1)

This filter establishes whether a potential group is larger than 1,000pixels. Only groups of that size and larger are considered as potentialthick groups. This filter is a part of the image segmenter.

Whole image feature box filters (2)

Two whole image-based boxes are used to reject all kinds of artifacts.These artifacts, called other by the algorithm, include cellularartifacts, non-cellular artifacts, and groups of normal cells that arepresent as thick groups. If:

    0≧-1.11×feature93+262-feature106

    0≦0.05×feature96+8-feature106

then the object is classified as other. If not, the object is passed tothe next box filter.

Out-of-focus box filters (3)

Three out-of-focus box filters are used to eliminate any segmentedobjects that are not properly in focus. These out-of-focus box filtersare designed to remove objects that were poorly focused during imageacquisition. Since identification of out-of-focus objects is unreliable,the algorithm should not attempt to classify them. The out-of-focusfilter, the cytoplasm filter, the graphite filter, and the poly filteruse one object feature in combination with either a whole image featureor another object-based feature: If:

    0≦-0.0027×feature70+0.427-feature7

    0≦-0.185×feature70+54.7-feature119

    0≦0.148×feature100+0.459-feature8

then the object is classified as other. If not, the object is passed tothe next box filter.

Cytoplasm box filters (2)

The algorithm uses two box filters to eliminate as many objects aspossible that are cytoplasm only artifacts: If:

    0≧27.3×feature98+218.4-feature93

    0≦-380×feature11+142-feature119

then the object is classified as other. If not, the object is passed tothe next box filter.

The graphite filter (1)

This filter removes objects that are graphite artifacts. Glasslaboratory slides of Pap smears commonly contain artifacts that prove tobe graphite particles left by pencils: If:

    0≧-12.2×feature33+106.11-feature95

then the object is classified as other. If not, the object is passed tothe next box filter.

Poly filter

The purpose of the poly filter is to eliminate segmented objects thatare polymorphonucleocytes white blood cells: If:

    0≧0.02×f96+8.5-feature22

then the object is classified as other. If not, the object is passed tothe next box filter.

Stage 1 Classifier

The stage 1 classifier is a Fisher's linear binary decision tree. Thestage 1 classifier 60 is designed to separate other objects--thickgroups of normal cells as well as cellular and non-cellularartifacts--from potentially abnormal groups. Stage 1's feature setconsists of the following 14 features:

    ______________________________________                                        feature 7                                                                            clus.sub.-- light.sub.-- 2.sub.-- dir                                                       feature 79                                                                              plus.sub.-- edge.sub.-- 17.sub.-- 17           feature 8                                                                            clus.sub.-- light.sub.-- 5.sub.-- mag                                                       feature 89                                                                              plus.sub.-- blur.sub.-- 15.sub.-- 15.sub.--                                    sd                                            feature 22                                                                           clus.sub.-- edge.sub.-- 9.sub.-- 9                                                          feature 93                                                                              high.sub.-- mean                               feature 24                                                                           clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.-- ave                                               feature 98                                                                              clus.sub.-- edge.sub.-- 2.sub.-- mag           feature 25                                                                           clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.-- sd                                                feature 100                                                                             clus.sub.-- edge.sub.-- 5.sub.-- mag           feature 70                                                                           cluster + brightness                                                                        feature 107                                                                             clus.sub.-- blue.sub.-- 3.sub.-- 3.sub.--                                     ave                                            feature 78                                                                           plus.sub.-- edge.sub.-- 9.sub.-- 9                                                          feature 119                                                                             image.sub.-- sd                                ______________________________________                                    

The features are described in Table 4.

Stage 2 Box Filters

Two pre-stage 2 box filters reject artifacts that made it through thebox filters and stage 1.

The box filters are implemented by the following rules, where if thestatement is true the object is rejected as artifact/normal:

    0≦0.6 feature102-1.04-feature112

    0≦0.025 feature13+0.21-feature66

    0≦0.025 feature121+0.099-feature42

Stage 2 Classifier

The stage 2 classifier 71 is a Fisher's linear decision tree. Stage 2uses the following 16 features:

    ______________________________________                                        feature 1                                                                            image.sub.-- sd                                                                            feature 81                                                                              plus.sub.-- blur.sub.-- 3.sub.-- 3.sub.--                                     sd                                              feature 8                                                                            clus.sub.-- light.sub.-- 5.sub.-- mag                                                      feature 88                                                                              plus .sub.-- blur.sub.-- 15.sub.-- 15.sub.--                                   ave                                            feature 30                                                                           clus.sub.-- blur.sub.-- 7.sub.-- 7.sub.-- sk                                               feature 93                                                                              high.sub.-- mean                                feature 58                                                                           ring.sub.-- blur.sub.-- 7.sub.-- 7.sub.-- sk                                               feature 104                                                                             clus.sub.-- edge.sub.-- 5.sub.-- 5              feature 66                                                                           ring.sub.-- polar.sub.-- max                                                               feature 107                                                                             clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.--                                     ave                                             feature 70                                                                           plus.sub.-- brightness                                                                     feature 119                                                                             image.sub.-- sd                                 feature 78                                                                           plus.sub.-- edge.sub.-- 9.sub.-- 9                                                         feature 120                                                                             image.sub.-- sk                                 feature 79                                                                           plus.sub.-- edge.sub.-- 17.sub.-- 17                                                       feature 121                                                                             image.sub.-- ku                                 ______________________________________                                    

Stage 3 Classifier

The Stage 3 classifier 72 is a Fisher's linear decision tree. Stage 3uses the following 9 features:

    ______________________________________                                        feature 1                                                                             area         feature 24                                                                             clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.--                                     ave                                             feature 12                                                                            clus.sub.-- light.sub.-- 3.sub.-- 3                                                        feature 32                                                                             clus.sub.-- blur.sub.-- 15.sub.-- 15.sub.--                                   ave                                             feature 15                                                                            clus.sub.-- edge.sub.-- 2.sub.-- mag                                                       feature 67                                                                             ring.sub.-- polar.sub.-- max.sub.-- 45          feature 16                                                                            clus.sub.-- edge.sub.-- 2.sub.-- dir                                                       feature 93                                                                             low.sub.-- threshold                            feature 19                                                                            clus.sub.-- edge.sub.-- 9.sub.-- mag                                  ______________________________________                                    

The thick group algorithm sends its accumulated results to a 20× FOVintegration algorithm. Its seven outputs are:

1. Number of objects eliminated by box filters.

2. Number of objects eliminated by the stage 1 classifier 60.

3. Number of objects eliminated by the stage 2 classifier 71.

4. Number of objects eliminated by the stage 3 classifier 72.

5. Number of potentially abnormal objects that remain after stage 3.

6. A bin confidence histogram of the remaining objects, which arepotentially abnormal cell groups. Confidence reflects the likelihood ofan object being abnormal and, as such, ranges from 0.5 to 1.0. Each nodein the decision tree is assigned a confidence value based on resultsduring training. For example, a confidence value of 0.9 would beassigned to a given node if it were found that during training, 90percent of the objects that terminated in that node were abnormalobjects. During normal operation, if an object terminated in this node,the object would be classed as potentially abnormal, and the 5th bin inthe confidence histogram (for confidence of 0.9 to 1.0) would beincremented. The range of confidence values assigned to each bin are asfollows:

Bin1 0.5<=confidence<0.6

Bin2 0.6<=confidence<0.7

Bin3 0.7<=confidence<0.8

Bin4 0.8<=confidence<0.9

Bin5 0.9<=confidence<=1.0

Since only the confidence histogram is retained on a slide it is notpossible to obtain the confidence assigned to any one abnormal object.

7. The invention checks to make certain that a proper return was madefrom the classifiers and performs a checksum to make certain that thenumber of objects processed is correct. Detection of an error causes anerror message to be printed, the algorithm to be aborted, and a -1returned to the algorithm dispatcher.

Thick Group Feature Library

Table 4 lists the entire library of features that were used in thickgroup algorithm development and classifier training. Detaileddescriptions of the features used by thick group processing follow Table4. Features computed for thick groups are a subset of those computed forgroup objects. In the following table, feature numbers for the thickgroup features are cross-referenced to the feature number for the samefeature used by the group processing.

The characters in the Type column indicate the feature type: o indicatesan object; i indicates a whole image.

                  TABLE 4                                                         ______________________________________                                        Thick Group Feature Library                                                   Thick                                                                         Group                                                                         Number Feature Name                                                           ______________________________________                                        feature 1                                                                            Cluster area                                                           feature 2                                                                            Cluster compactness                                                    feature 4                                                                            Standard deviation of cluster intensity                                feature 5                                                                            Cluster brightness                                                     feature 6                                                                            Magnitude of 2 × 1 bright edge in cluster                        feature 7                                                                            Directional disparity of 2 × 1 bright edge in cluster            feature 8                                                                            Magnitude of 5 × 1 bright edge in cluster                        feature 9                                                                            Directional disparity of 5 × 1 bright edge in cluster            feature 10                                                                           Magnitude of 9 × 1 bright edge in cluster                        feature 11                                                                           Directional disparity of 9 × 1 bright edge in cluster            feature 12                                                                           3 × 3 bright edge in cluster                                     feature 13                                                                           9 × 9 bright edge in cluster                                     feature 14                                                                           17 × 17 bright edge in cluster                                   feature 15                                                                           Magnitude of 2 × 1 dark edge in cluster                          feature 16                                                                           Directional disparity of 2 × 1 dark edge in cluster              feature 17                                                                           Magnitude of 5 × 1 dark edge in cluster                          feature 18                                                                           Directional disparity of 5 × 1 dark edge in cluster              feature 19                                                                           Magnitude of 9 × 1 dark edge in cluster                          feature 20                                                                           Directional disparity of 9 × 1 dark edge in cluster              feature 21                                                                           5 × 5 dark edge in cluster                                       feature 22                                                                           9 × 9 dark edge in cluster                                       feature 23                                                                           17 × 17 dark edge in cluster                                     feature 24                                                                           3 × 3 blur residue mean in cluster                               feature 25                                                                           3 × 3 blur residue standard deviation in cluster                 feature 26                                                                           3 × 3 blur residue skewness in cluster                           feature 27                                                                           3 × 3 blur residue kurtosis in cluster                           feature 28                                                                           7 × 7 blur residue mean in cluster                               feature 29                                                                           7 × 7 blur residue standard deviation in cluster                 feature 30                                                                           7 × 7 blur residue skewness in cluster                           feature 31                                                                           7 × 7 blur residue kurtosis in cluster                           feature 32                                                                           15 × 15 blur residue mean in cluster                             feature 33                                                                           15 × 15 blur residue standard deviation in cluster               feature 34                                                                           15 × 15 blur residue skewness in cluster                         feature 35                                                                           15 × 15 blur residue kurtosis in cluster                         feature 36                                                                           Polarity area in cluster                                               feature 37                                                                           Polarity range in cluster                                              feature 38                                                                           Polarity maximum in cluster                                            feature 39                                                                           Polarity in maximum direction + 45° in cluster                  feature 40                                                                           Polarity in maximum direction + 90° in cluster                  feature 41                                                                           Polarity in maximum direction + 135° in cluster                 feature 42                                                                           Normalized cluster brightness                                          feature 43                                                                           Magnitude of 2 × 1 dark edge in normalized cluster               feature 44                                                                           Directional disparity of 2 × 1 dark edge in normalized                  cluster                                                                feature 45                                                                           Magnitude of 5 × 1 dark edge in normalized cluster               feature 46                                                                           Directional disparity of 5 × 1 dark edge in normalized                  cluster                                                                feature 47                                                                           Magnitude of 9 × 1 dark edge in normalized cluster               feature 48                                                                           Directional disparity of 9 × 1 dark edge in normalized                  cluster                                                                feature 49                                                                           5 × 5 dark edge in normalized cluster                            feature 50                                                                           9 × 9 dark edge in normalized cluster                            feature 51                                                                           17 × 17 dark edge in normalized cluster                          feature 52                                                                           3 × 3 blur residue mean in normalized cluster                    feature 53                                                                           3 × 3 blur residue standard deviation in normalized cluster      feature 54                                                                           3 × 3 blur residue skewness in normalized cluster                feature 55                                                                           3 × 3 blur residue kurtosis in normalized cluster                feature 56                                                                           7 × 7 blur residue mean in normalized cluster                    feature 57                                                                           7 × 7 blur residue standard deviation in normalized cluster      feature 58                                                                           7 × 7 blur residue skewness in normalized cluster                feature 59                                                                           7 × 7 blur residue kurtosis in normalized cluster                feature 60                                                                           15 × 15 blur residue mean in normalized cluster                  feature 61                                                                           15 × 15 blur residue standard deviation in normalized                   cluster                                                                feature 62                                                                           15 × 15 blur residue skewness in normalized cluster              feature 63                                                                           15 × 15 blur residue kurtosis in normalized cluster              feature 64                                                                           Polarity area in ring around cluster                                   feature 65                                                                           Polarity range in ring around cluster                                  feature 66                                                                           Polarity maximum in ring around cluster                                feature 67                                                                           Polarity in maximum direction + 45° in ring around cluster      feature 68                                                                           Polarity in maximum direction + 90° in ring around cluster      feature 69                                                                           Polarity in maximum direction + 135° in ring around                    cluster                                                                feature 70                                                                           cluster + brightness                                                   feature 71                                                                           Magnitude of 2 × 1 bright edge in cluster+                       feature 72                                                                           Directional disparity of 2 × 1 dark edge in cluster+             feature 73                                                                           Magnitude of 5 × 1 dark edge in cluster+                         feature 74                                                                           Directional disparity of 5 × 1 dark edge in cluster+             feature 75                                                                           Magnitude of 9 × 1 dark edge in cluster+                         feature 76                                                                           Directional disparity of 9 × 1 dark edge in cluster+             feature 77                                                                           5 × 5 dark edge in cluster+                                      feature 78                                                                           9 × 9 dark edge in cluster+                                      feature 79                                                                           17 × 17 dark edge in cluster+                                    feature 80                                                                           3 × 3 blur residue in cluster+                                   feature 81                                                                           3 × 3 blur residue standard deviation in cluster+                feature 82                                                                           3 × 3 blur residue skewness in cluster+                          feature 83                                                                           3 × 3 blur residue kurtosis in cluster+                          feature 84                                                                           7 × 7 blur residue mean in cluster+                              feature 85                                                                           7 × 7 blur residue standard deviation in cluster+                feature 86                                                                           7 × 7 blur residue skewness in cluster+                          feature 87                                                                           7 × 7 blur residue kurtosis in cluster+                          feature 88                                                                           15 × 15 blur residue mean in cluster+                            feature 89                                                                           15 × 15 blur residue standard deviation in cluster+              feature 90                                                                           15 × 15 blur residue skewness in cluster+                        feature 91                                                                           15 × 15 blur residue kurtosis in cluster+                        feature 92                                                                           SIL high.sub.-- count variable                                         feature 93                                                                           SIL high.sub.-- mean variable                                          feature 94                                                                           SIL medium.sub.-- threshold variable                                   feature 95                                                                           SIL low.sub.-- threshold variable                                      feature 96                                                                           FOV brightness                                                         feature 97                                                                           FOV edge                                                               feature 98                                                                           Magnitude of 2 × 1 dark edge in FOV                              feature 99                                                                           Directional disparity of 2 × 1 dark edge in FOV                  feature                                                                              Magnitude of 5 × 1 dark edge in FOV                              100                                                                           feature                                                                              Directional disparity of 5 × 1 dark edge in FOV                  101                                                                           feature                                                                              Magnitude of 9 × 1 dark edge in FOV                              102                                                                           feature                                                                              Directional disparity of 9 × 1 dark edge in FOV                  103                                                                           feature                                                                              5 × 5 dark edge in FOV                                           104                                                                           feature                                                                              9 × 9 dark edge in FOV                                           105                                                                           feature                                                                              17 × 17 dark edge in FOV                                         106                                                                           feature                                                                              3 × 2 blur residue mean                                          107                                                                           feature                                                                              3 × 3 blur residue standard deviation in FOV                     108                                                                           feature                                                                              3 × 3 blur residue skewness in FOV                               109                                                                           feature                                                                              3 × 3 blur residue kurtosis in FOV                               110                                                                           feature                                                                              7 × 7 blur residue mean in FOV                                   111                                                                           feature                                                                              7 × 7 blur residue standard deviation in FOV                     112                                                                           feature                                                                              7 × 7 blur residue skewness in                                   113                                                                           feature                                                                              7 × 7 blur residue kurtosis in FOV                               114                                                                           feature                                                                              15 × 15 blur residue mean in FOV                                 115                                                                           feature                                                                              15 × 15 blur residue standard deviation in FOV                   116                                                                           feature                                                                              15 × 15 blur residue skewness in FOV                             117                                                                           feature                                                                              15 × 15 blur residue kurtosis in FOV                             118                                                                           feature                                                                              Whole image standard deviation                                         119                                                                           feature                                                                              Whole image skewness                                                   120                                                                           feature                                                                              Whole image kurtosis                                                   121                                                                           ______________________________________                                    

Thick Group Feature Descriptions

The following are feature descriptions for all features that wereselected from the feature library during training. They are arranged bythick group feature number (Feature 1 through Feature 121). Thick groupfeatures are cross referenced with the feature name and the featuretype. Features of type "O" are based on the object segmentation mask,whereas objects of type "i" are based on the entire 20× FOV.

Feature Descriptions

    ______________________________________                                        Feature Number   Feature Name                                                                             Type                                              ______________________________________                                        feature1         area       O                                                 ______________________________________                                    

Feature 1 is the area, in pixels, of the cluster mask. Feature 1 is usedby the stage 2 and stage 3 classifiers.

    ______________________________________                                        feature7       clus.sub.-- light.sub.-- 2.sub.-- dir                                                     O                                                  ______________________________________                                    

Feature 7 is the 2×1 (2 pixels horizontally by 1 pixel vertically)bright edge directional disparity within the cluster. Bright edgedirectional disparity is a combination of two measures. Pixels areexamined to find those that have darker neighbors on both sideshorizontally, then those that have darker neighbors vertically. For eachpixel that passes the neighbor test, the magnitude of the difference isrecorded. The magnitude of differences for all horizontal pixels aresummed. Then all pixels in the vertical are summed. Feature 7 iscalculated as the minimum of these two values divided by the sum of thetwo. It provides a measure of whether there are significantly morerelatively bright pixels in one direction versus the other. This featureshows whether there is some directionally dominant texture in thecluster. In this case, the texture is very fine, or of a high spatialfrequency. Feature 7 is used by one of the out-of-focus box filters andby the stage 1 classifier.

    ______________________________________                                        feature8      clus.sub.-- light.sub.-- 5.sub.-- mag                                                      O                                                  ______________________________________                                    

Feature 8 is the 5×1 bright edge magnitude. As with the directionaldisparity described in Feature 7, this measure is made up of twodirections: horizontal and vertical. In this case, rather than lookingfor pixels that are surrounded by dark pixels in one direction, groupsof three pixels are examined to see if they are bounded by dark pixelson both sides horizontally and vertically. Feature 8 is calculated bysquaring the two measures, summing them, then taking their square root.This feature gives a measure of how much edge there is in the clusterthat is about three pixels wide in either direction. It also gives ameasure for the amount of texture there is that has bright spots aboutthree pixels in size. Feature 8 is used by one of the out-of-focus boxfilters, and by the stage 1 and stage 2 classifiers.

    ______________________________________                                        feature11      clus.sub.-- light.sub.-- 9.sub.-- dir                                                     O                                                  ______________________________________                                    

Feature 11 is similar to feature 7 except that groups of seven pixelsare checking for dark neighbors rather than a single pixel. Feature 11is used by the cytoplasm box filter.

    ______________________________________                                        feature12      clus.sub.-- light.sub.-- 3.sub.-- 3                                                       O                                                  ______________________________________                                    

Feature 12 is the 3×3 bright edge strength in the cluster. The algorithmsearches for pixels that have dark pixels around them in all directions.The difference between the bright pixel and its surrounding pixels areaccumulated for all such pixels in the cluster. The accumulated figureis normalized by the total number of pixels in the cluster. Thismeasures the amount of texture in each cluster that consists of brightregions about one pixel in size that are surrounded by darker pixels onall sides. Feature 12 is used by the poly box filter.

    ______________________________________                                        feature13      clus.sub.-- light.sub.-- 9.sub.-- 9                                                       O                                                  ______________________________________                                    

Feature 13 is similar to feature 12 except that groups of pixels, 7×7 insize, are checked for darker neighbors. Feature 13 is used by the stage2 pre-box filter.

    ______________________________________                                        feature15     clus.sub.-- edge.sub.-- 2.sub.-- mag                                                       O                                                  ______________________________________                                    

Feature 15 is the magnitude of the 2×1 dark edge. This feature is thesame as Feature 8 except that single, dark pixels are searched forrather than bright regions 3 pixels wide. This measure is of the totalamount of dark area covered by single pixels bounded in two directionsby bright area. Feature 15 is used by the stage 3 classifier.

    ______________________________________                                        feature16     clus.sub.-- edge.sub.-- 2.sub.-- dir                                                       O                                                  ______________________________________                                    

Feature 16 is the directional disparity of 2×1 dark edge in cluster. Thefeature is similar to feature 7 with the exception that the pixels areexamined to find those that have brighter neighbors. This feature isused by the stage 3 classifier.

    ______________________________________                                        feature19     clus.sub.-- edge.sub.-- 9.sub.-- mag                                                       O                                                  ______________________________________                                    

Feature 19 is the magnitude of the 9×1 dark edge. This is the same asfeature 15 except that regions of 7 pixels in width or height aresearched for that have bright neighbors. This feature is used by thestage 3 classifier.

    ______________________________________                                        feature22      clus.sub.-- edge.sub.-- 9.sub.-- 9                                                        O                                                  ______________________________________                                    

Feature 22 is 9×9 dark edge strength. This is the same as feature 12except that pixels with brighter neighbors are searched for and the sizeof the dark region searched for is about 7×7. The texture this featuremeasures are dark spots about 4 microns on a side. Feature 22 is used bythe poly box filter and by the stage 1 classifier.

    ______________________________________                                        feature24     clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.-- ave                                               O                                                 ______________________________________                                    

Feature 24 is called mean 3×3 blur residue in the cluster. The algorithmmeasures the absolute difference between a 3×3 binomial filtered imageand its original. The average pixel value of this difference is feature24. This feature measures high spatial frequency in the cluster. Feature24 is used by the stage 1 and stage 2 classifiers.

    ______________________________________                                        feature25     clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.-- sd                                               O                                                  ______________________________________                                    

Feature 25 is the standard deviation of the 3×3 blur residue in thecluster. This measure gives some indication of how uniform high spatialfrequencies are within the cluster. Feature 25 is used by the stage 1classifier.

    ______________________________________                                        feature30     clus.sub.-- blur.sub.-- 7.sub.-- 7.sub.-- sk                                               O                                                  ______________________________________                                    

Feature 30 is the 7×7 blur residue skewness in cluster. The image isblurred using a 7×7 structure element. The difference between this andthe original image is taken. The feature is the skewness of thisdifference in the area defined by the object mask. Feature 30 is used bythe stage 2 classifier.

    ______________________________________                                        feature32     clus.sub.-- blur.sub.-- 15.sub.-- 15.sub.-- ave                                              O                                                ______________________________________                                    

Feature 32 is the 15×15 blur residue mean in cluster. It is similar tofeature 24 except that this feature uses a 15×15 structure element toperform the filtering. Feature 32 is used by the stage 3 classifier.

    ______________________________________                                        feature33     clus.sub.-- blur.sub.-- 15.sub.-- 15.sub.-- sd                                              O                                                 ______________________________________                                    

Feature 33 is similar to feature 32 except that standard deviation iscomputed. Feature 33 is used by the graphite box filter.

    ______________________________________                                        feature42       ring.sub.-- brightn                                                                     O                                                   ______________________________________                                    

Feature 42 is the average brightness of the normalized cluster. Theaverage intensity of the cluster is divided by the average intensity ofthe ring around the cluster. This ratio is average brightness. Feature42 is used by the pre-stage 2 box filters.

    ______________________________________                                        feature58     ring.sub.-- blur.sub.-- 7.sub.-- 7.sub.-- sk                                               O                                                  ______________________________________                                    

Feature 58 is the 7×7 blur residue skewness. This is the ratio of the7×7 blur residue skewness of the cluster to that of the ring around thecluster. This ratio indicates how texture variations compare from thecluster to the ring around the cluster. It identifies changes in texturefrom the cluster to the ring around the cluster. It also provides ameasure of how similar the cluster is to its background when its spatialfrequencies have been eliminated by a 7×7 filter. Feature 58 is used bythe stage 2 classifier.

    ______________________________________                                        feature66      ring.sub.-- polar.sub.-- max                                                              O                                                  ______________________________________                                    

Feature 66 is the maximum polarity in the ring around the cluster. Thisfeature measures the amount of dark ovoid area that is within the ringaround the cluster. The polarity is broken down into its maximumdirection, its direction perpendicular to the maximum, and its directionto 45 degrees on either side of the maximum. Polarity in the maximumdirection in the ring around the cluster gives the value for feature 66.Feature 66 is used by the pre-stage 2 box filters and by the stage 2classifier.

    ______________________________________                                        feature67     ring.sub.-- polar.sub.-- max.sub.-- 45                                                      O                                                 ______________________________________                                    

Feature 67 is polarity in maximum direction plus 45 degrees in ringaround cluster. Similar to feature 66 except the polarity is in thedirection of the maximum plus 45 degrees. Feature 67 is used by thestage 3 classifier.

    ______________________________________                                        feature70      plus.sub.-- brightness                                                                    O                                                  ______________________________________                                    

Feature 70 is cluster plus brightness. This is the average intensity ofthe pixels in the expanded cluster. Feature 70 is used by the stage 1classifier.

    ______________________________________                                        feature78      plus.sub.-- edge.sub.-- 9.sub.-- 9                                                        O                                                  ______________________________________                                    

Feature 78 is called 9×9 dark edge strength. This is the same as feature22 except that it's calculated in the expanded cluster rather than inthe cluster. Feature 78 is used by the stage 1 classifier and by thestage 2 classifier.

    ______________________________________                                        feature79     plus.sub.-- edge.sub.-- 17.sub.-- 17                                                       O                                                  ______________________________________                                    

Feature 79 is a 17×17 dark edge in a cluster. The feature is the same asfeature 78 except a 17×17 dark region is searched for. Feature 79 isused by the stage 1 and stage 2 classifiers.

    ______________________________________                                        feature81     plus.sub.-- blur.sub.-- 3.sub.-- 3.sub.-- sd                                               O                                                  ______________________________________                                    

Feature 81 is a 3×3 blur residue standard deviation in the expandedcluster. The difference between the original image and the image blurredby a 3×3 filter is taken. The feature is the standard deviation of thepixels in the expanded cluster. Feature 81 is used by the stage 2classifier.

    ______________________________________                                        feature88     plus.sub.-- blur.sub.-- 15.sub.-- 15.sub.-- ave                                              O                                                ______________________________________                                    

Feature 88 is 15×15 blur residue mean in the expanded cluster. Thedifference between the original image and the image blurred by a 15×15filter is taken. The feature is the standard deviation of the pixels inthe expanded cluster. Feature 88 is used by the stage 2 classifier.

    ______________________________________                                        feature89     plus.sub.-- blur.sub.-- 15.sub.-- 15.sub.-- sd                                              O                                                 ______________________________________                                    

Feature 89 is a 15×15 blur residue standard deviation in the expandedcluster. Similar to feature 81 except a 15×15 filer is used. Feature 89is used by the stage 1 classifier.

    ______________________________________                                        feature93       high.sub.-- mean                                                                        i                                                   ______________________________________                                    

Feature 93 is the single cell algorithm's high₋₋ mean variablemeasurement. This is the average value of all pixels in an image thathave values between 199 and 250. This feature provides some informationabout an image's background. Feature 93 is used by one of the cytoplasmbox filters, and by the stage 1 and stage 2 classifiers.

    ______________________________________                                        feature95       low.sub.-- threshold                                                                     i                                                  ______________________________________                                    

Feature 95 is the single cell algorithm's low₋₋ threshold value. Thisvalue is calculated during single cell segmentation. It is the result ofan adaptive threshold calculation for a certain range of pixelintensities in an image. It gives a measure for how much dark matterthere is in an image. If the threshold is low, there is a fair amount ofdark matter in the image. If the threshold is high, there are probablyfew high density objects in the image. Feature 95 is used by thegraphite box filter and the stage 3 classifier.

    ______________________________________                                        feature98      clus.sub.-- edge.sub.-- 2.sub.-- mag                                                       i                                                 ______________________________________                                    

Feature 98 is the magnitude of the 2×1 dark edge in an image. Thisfeature is calculated in the same way as feature 15 except that it iscalculated over the whole image. Feature 98 is used by the stage 1classifier.

    ______________________________________                                        feature100     clus.sub.-- edge.sub.-- 5.sub.-- mag                                                       i                                                 ______________________________________                                    

Feature 100 is the magnitude of the 5×1 dark edge in an FOV. Thisfeature is calculated the same way as feature 15 except that dark3-pixel regions are searched for instead of single dark pixel. Feature100 is used by the stage 1 classifier.

    ______________________________________                                        feature104     clus.sub.-- edge.sub.-- 5.sub.-- 5                                                        i                                                  ______________________________________                                    

Feature 104 is 5×5 dark edge strength. This feature is computed byfinding 3×3 clusters of pixels that are surrounded by brighter pixels.The difference between the surrounding pixels and each pixel in thecluster is computed. This difference is accumulated for all the pixelsin the cluster and normalized by the number of pixels in the wholeimage. The feature characterizes texture with dark spots that measure 1to 2 microns on a side. Feature 104 is used by the stage 2 classifier.

    ______________________________________                                        feature106     clus.sub.-- edge.sub.-- 17.sub.-- 17                                                       i                                                 ______________________________________                                    

Feature 106 is a 17×17 dark edge strength. This feature is the same asfeature 104 except that the accumulation is based on clusters that are17×17 pixels in size. Feature 106 is used by the area box filter and bytwo whole image box filters.

    ______________________________________                                        feature107    clus.sub.-- blur.sub.-- 3.sub.-- 3.sub.-- ave                                               i                                                 ______________________________________                                    

Feature 107 is a 3×3 blur residue mean. This is calculated the same wayas feature 24 except that the feature is calculated over the entireimage rather than just over the cluster. Feature 107 is used by thestage 1 and stage 2 classifiers.

    ______________________________________                                        feature109     clus.sub.-- blue.sub.-- 3.sub.-- 3.sub.-- sk                                               i                                                 ______________________________________                                    

Feature 109 is similar to feature 107 except the skewness instead of themean is computed. Feature 109 is used by the pre-stage 2 box filters.

    ______________________________________                                        feature112     clus.sub.-- blur.sub.-- 7.sub.-- 7.sub.-- sd                                               i                                                 ______________________________________                                    

Feature 112, a blur residue using a 7×7 structure element, is computedfor a whole image. Feature 112 is the standard deviation of thisfeature. Feature 112 is used by the pre-stage 2 box filters.

    ______________________________________                                        feature119       image.sub.-- sd                                                                        i                                                   ______________________________________                                    

Feature 119 is the standard deviation for the whole image. This is thestandard deviation of the pixel values for every pixel in an image. Thisfeature provides a measure of the amount of variation in pixel intensityacross the entire 20× FOV. Feature 119 is used by one of theout-of-focus box filters, the cytoplasm box filters, and by stage 1 andstage 2 classifiers.

    ______________________________________                                        feature120       image.sub.-- sk                                                                        i                                                   ______________________________________                                    

Feature 120 is the whole image skewness. This feature is the skewness ofthe values of every pixel in an image. It is a measure of how much pixelvalues are biased to one side or the other of mean pixel intensity.Skewness provides a measure of image content. Feature 120 is used by thestage 2 classifier.

    ______________________________________                                        feature121       image.sub.-- ku                                                                        i                                                   ______________________________________                                    

Feature 121 is a whole image kurtosis. The feature is the 4thstatistical moment or kurtosis taken on the whole image. Kurtosisprovides a measure of the percentage of the population that is in thetails of the distribution. Feature 175 is used by the stage 2classifier.

The invention has been described herein in considerable detail in orderto comply with the Patent Statutes and to provide those skilled in theart with the information needed to apply the novel principles and toconstruct and use such specialized components as are required. However,it is to be understood that the invention can be carried out byspecifically different equipment and devices, and that variousmodifications, both as to the equipment details and operatingprocedures, can be accomplished without departing from the scope of theinvention itself.

What is claimed is:
 1. In an automated system for analyzing a biologicalspecimen having a thick group of cells, a thick group of cellsclassification apparatus comprising:(a) an automated microscope havingat least one image output; (b) image segmentation means for identifyingobjects of interest connected to the image output, wherein the imagesegmentation means has a segmented image output; (c) object featurecalculation means for measuring properties of each segmented objecthaving a segmented object output wherein the object feature calculationmeans has a thick group of cells object feature output; and (d) anobject classification means for classifying thick group objects foranalysis having an input connected to the thick group of cells objectfeature output, where the object classification means has a thick groupof cells classification output, and where the object classificationmeans comprises a multiple stage algorithmic classifier, wherein everystage is an algorithmic classifier and wherein at least one of thestages comprises a Fisher's binary decision tree classifier.
 2. Theapparatus of claim 1 wherein the biological specimen is a specimenprepared by the Papanicolaou method.
 3. The apparatus of claim 1 whereinthe biological specimen is a gynecological specimen.
 4. The apparatus ofclaim 1 wherein the object classification means further comprises adigital computer.
 5. An apparatus for training a system to recognizethick groups of interest comprising:(a) means for acquiring at least oneimage from at least one biological specimen; (b) means for detecting atleast one thick group of cells for analysis from the at least one image,wherein the means for detecting has a thick group of cells output; (c)means for obtaining diagnostic truth connected to the thick group ofcells output, wherein the means for obtaining diagnostic truth has atruth established thick group of cells output; (d) a means for computingfeatures connected to the truth established thick group of cells output,wherein the means for computing features has a feature output; (e) meansfor selecting features that best discriminate between thick groups ofinterest and other groups, wherein the means for selecting features hasa discrimination output; and (f) classification means for separatingthick groups of interest from other groups connected to thediscrimination output, wherein the classification means has aclassification output, where the object classification means comprises amultiple stage algorithmic classifier wherein every stage is analgorithmic classifier, and wherein at least one of the stages comprisesa Fisher's binary decision tree classifier.
 6. The apparatus fortraining a system to recognize thick groups of interest of claim 5further comprising means for determining an overall gain for theclassification means.
 7. The apparatus of claim 5 wherein the thickgroup of interest comprises an abnormal group of cells.
 8. The apparatusof claim 5 wherein the thick group of interest comprises an artifact. 9.The apparatus of claim 5 wherein the thick group of interest comprises athick group of normal cells.
 10. The apparatus of claim 5 wherein thebiological specimen is a specimen prepared by the Papanicolaou method.11. The apparatus of claim 5 wherein the biological specimen is agynecological specimen.
 12. The apparatus of claim 5 wherein the thickgroup detection means further comprises a digital computer.
 13. In anautomated system for analyzing a biological specimen having a thickgroup of cells, a thick group of cells classification process comprisingthe steps of:(a) obtaining at least one image output; (b) segmenting theat least one image output to identify objects of interest; (c) measuringproperties of each segmented object of interest to identify a pluralityof object features indicative of a thick group of cells; and (d)processing the plurality of object features indicative of a thick groupof cells to classify thick group objects for analysis by applying amultiple stage algorithmic classifier, wherein every stage is analgorithmic classifier and wherein at least one of the stages comprisesa Fisher's binary decision tree classifier.
 14. The process of claim 13wherein the biological specimen is a specimen prepared by thePapanicolaou method.
 15. The apparatus of claim 13 wherein thebiological specimen is a gynecological specimen.
 16. The apparatus ofclaim 13 wherein the object classification means further comprises adigital computer.